Senior AI/ML Engineer

Optum India; Hyderabad; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Optum India is seeking a senior AI/ML researcher to lead end-to-end LLM training and fine-tuning across open-source and closed-source ecosystems.

You will architect GraphRAG pipelines, design semantic embeddings, and scale distributed multi-GPU training, applying RLHF/RLAIF and collaborating with cross-functional teams to deploy in production.

Qualifications

• PhD or Master’s degree in computer science, machine learning, or related field.
• Experience in applied AI/ML with statistics, with a solid track record of delivering production-grade models.
• Experience with Hybrid NLP solutions that combine symbolic and machine learning approaches.
• Deep knowledge of transformer architectures, state space models, large language models, and agentic approaches.
• Proven Python coding, SQL and data analysis skills.
• LLM training and fine-tuning (e.g., GPT, LLaMA, Mistral, Qwen).

Responsibilities

• Develop end-to-end training and fine-tuning of Large Language Models (LLMs), including open-source and closed-source ecosystems.
• Architect and implement GraphRAG pipelines with knowledge graphs for contextual grounding.
• Design, train, and optimize semantic and dense vector embeddings for document understanding, search, and retrieval.
• Develop semantic retrieval systems with advanced document segmentation and indexing strategies.
• Build and scale distributed training environments using NCCL and InfiniBand for multi-GPU and multi-node training.
• Apply reinforcement learning techniques (RLHF, RLAIF) to align model behavior with human preferences and domain goals.
• Collaborate with cross-functional teams to translate business needs into AI-driven solutions and deploy in production environments.

Skills

Python

SQL

EDA

NLP

Deep Learning

Transformer

RLHF

Distributed Training

NCCL

MLOps

Education

PhD or Master’s in CS/ML

Tools

PyTorch

FAISS

Weaviate

Milvus

GraphRAG

Job description

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together.

Primary Responsibilities:

• Develop end-to-end training and fine-tuning of Large Language Models (LLMs), including both open-source (e.g., Qwen, LLaMA, Mistral) and closed-source (e.g., OpenAI, Gemini, Anthropic) ecosystems
• Architect and implement GraphRAG pipelines, including knowledge graph representation and retrieval for enhanced contextual grounding
• Design, train, and optimize semantic and dense vector embeddings for document understanding, search, and retrieval
• Develop semantic retrieval systems with advanced document segmentation and indexing strategies
• Build and scale distributed training environments using NCCL and InfiniBand for multi-GPU and multi-node training
• Apply reinforcement learning techniques (e.g., RLHF, RLAIF) to align model behavior with human preferences and domain-specific goals
• Collaborate with cross-functional teams to translate business needs into AI-driven solutions and deploy them in production environments

Required Qualifications:

• PhD or Master’s degree in computer science, Machine Learning, or related field
• Experience in applied AI/ML with statistics, with a Solid track record of delivering production-grade models
• Experience with Hybrid NLP solutions that combine symbolic and machine learning approaches
• Experience with PyTorch
• Deep knowledge and extensive experience with Machine/Deep Learning frameworks including transformer architectures, state space models, large language models, and agentic approaches
• Knowledge of algorithms and techniques within a computational domain with emphasis on text processing
• Proven deep expertise in: NLP, Fundamental machine learning, deep learning, transformer, state space-based architecture
• Proven solid in Python coding, SQL and database queries, data preparation, and analysis
• Exploratory Data Analysis (EDA)
• LLM training and fine-tuning (e.g., GPT, LLaMA, Mistral, Qwen)
• Graph-based retrieval systems (GraphRAG, knowledge graphs)
• Semantic search and vector databases (e.g., FAISS, Weaviate, Milvus)
• Document segmentation and preprocessing (OCR, layout parsing)
• Model fusion and ensemble techniques (stacking, boosting, gating)
• Optimization algorithms (Bayesian, Particle Swarm, Genetic Algorithms)
• Reinforcement learning (e.g., RLHF, PPO, DPO, GRPO), Supervised Fine Tuning (SFT), LoRA, QLoRA, axolotl